The posting, in Fospha Marketing's own words
archived Sep 7, 2026Fospha is the measurement system enterprise retail and ecommerce brands run their business on. We give marketing teams one clear, daily view of what's actually working — across every channel and everywhere they sell, from their website to Amazon and TikTok Shop — down to the level of a single ad or piece of creative. It replaces guesswork and gut feel with a number marketing, finance and agencies can all trust and act on. Brands including Dyson, Gymshark and CarParts use Fospha up to 25 times a day to decide where budget should move next. We've spent over a decade building this, with more than $40 billion in marketing spend now optimised through the platform — and we're scaling fast across London, Mumbai and Austin.
About the role
We're looking for a Graduate Marketing Scientist to join Fospha's Marketing Science team in London. Fospha builds marketing measurement products for ecommerce brands — attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Marketing Science owns the applied end of that: designing and delivering incrementality tests and MMM engagements for clients, and standing behind the numbers when a client challenges them. This is the entry point into the function, and it is a hands-on one. You'll work on live test and MMM delivery under supervision from the start, and you'll be the first person looking at a client's data when a number doesn't behave the way it should. It's a role for someone who wants to learn causal measurement properly, in a business where it's the product rather than a side project. Team: Marketing Science Level: Graduate — Entry (Data Science Career Development Framework) Location: London
Read the full posting ↓
Marketing mix modelling (MMM) & Testing Services
Assemble and validate test data — geo-level spend and conversion series, checking pre-period parity between treatment and control, spotting the coverage gaps that invalidate a design before it launches Support test design under review — market matching and control selection, power and minimum detectable effect sanity checks, and identifying contamination risks such as geo-targeting settings that don't behave the way the platform's documentation claims Run analysis and read the results honestly — pre-treatment fit diagnostics, lift estimates with their intervals, and what a null result does and doesn't tell you Qualify client data for MMM — spend coverage across channels, whether there's enough variation in spend to identify an effect at all, series length and granularity, collinearity between channels, and gaps that will bias the result Assemble and validate model input datasets , and investigate the discrepancies that surface when you do Support model runs and read the diagnostics — fit, residuals, convergence, and whether a channel's estimated contribution is plausible Contribute to output-extension work under review — building on an existing MMM result, for example forecasting or budget scenario work derived from it Compare results across methods — where MMM, incrementality, and platform-reported figures disagree, understanding why is the interesting part of the job
Model trust and diagnostics
First and second line on client trust queries — investigating why a number changed, working in SQL against client data to isolate the cause Distinguish a bug from a methodology change — attribution window changes, model recalibration, data feed gaps, and platform reporting shifts all look similar from the outside and have very different signatures underneath Triage PSPs on model trust , resolve what you can, and escalate what turns out to be a genuine model problem with a clear diagnosis attached Reconcile platform-reported figures against our measurement — why walled-garden ROAS disagrees with ours is the hardest recurring question in the business, and you'll be learning it from the inside Log and tag incidents consistently , so recurring failure patterns become visible and can be automated away rather than repeatedly handled
Client communication and enablement
Run templated explainer sessions under review, walking clients through how our measurement works Draft documentation and presentations above the core explainer content, and feed recurring query themes back into the source material Fact-check methodology claims in product marketing collateral before it goes out